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Transcript of Evaluation issues in anaphora resolution and beyond Ruslan Mitkov University of Wolverhampton Faro,...
Evaluation issues in
anaphora resolution and beyond
Ruslan Mitkov
University of Wolverhampton
Faro, 27 June 2002
Evaluation
Evaluation is a driving force for every NLP task/approach/application
Evaluation is indicative of the performance of a specific approach/application but not less importantly, reports where it stands as compared to other approaches/applications
Growing research in evaluation inspired by the availability of annotated corpora
Major impediments to fulfilling evaluation’s mission
Different approaches evaluated on different data
Different approaches evaluated in different
modes Results not independently confirmed As a result, no comparison or objective
evaluation possible
Anaphora resolution vs. coreference resolution
• Anaphora resolution has to do with tracking
down an antecedent of an anaphor
• Coreference resolution seeks to identify all
coreference classes (chains)
Anaphora resolution
For nominal anaphora which involves coreference it would be logical to regard each of the preceding noun phrases which are coreferential with the anaphor(s) as a legitimate antecedent Computational Linguists from many different countries attended PorTAL. The participants enjoyed the presentations; they also took an active part in the discussions.
Evaluation in anaphora resolution
Two perspectives:
• Evaluation of anaphora resolution algorithms
• Evaluation of anaphora resolution systems
Recall and Precision
MUC introduced the measures recall and
precision for coreference resolution.
These measures, as defined, are not
satisfactory in terms of clarity and
coverage (Mitkov 2001).
Evaluation package for anaphora resolution algorithms (Mitkov 1998; 2000)
Evaluation package for anaphora resolution
algorithms
(i) performance measures
(ii) comparative evaluation tasks and
(iii) component measures.
Performance measures
Success rate
Critical success rate
Critical success rate applies only to those ‘tough’
anaphors which still have more than one
candidate for antecedent after gender and
number filter
Example
• Evaluation data: 100 anaphors • Number of anaphors correctly resolved: 80• Number of anaphors correctly resolved
after gender and number constraints: 30
Success rate: 80/100 = 80%,
Critical success rate 50/70 = 71.4%
Comparative evaluation tasks
• Evaluation against baseline models • Comparison to similar approaches • Comparison with well-established approaches
Approaches frequently used for comparison:
Hobbs (1978), Brenan et al. (1987), Lappin and Leass (1994), Kennedy and Boguraev (1996), Baldwin (1997), Mitkov (1996; 1998)
Component measures
• Relative importance
• Decision power (Mitkov 2001)
Evaluation measures for anaphora resolution systems
• Success rate
• Critical success rate
• Resolution etiquette (Mitkov et al. 2002)
Reliability of evaluation results
Evaluation results can be regarded as reliable if evaluation covers/employs
(i) All naturally occurring texts
(ii) Sampling procedures
Relative vs. absolute results
• Results may be relative with regard to a specific evaluation set or other approach
• More “absolute” figures may be obtained if there existed a measure which quantified for the complexity of anaphors to be resolved
Measures quantifying complexity in anaphora resolution
Measures for complexity (Mitkov 2001):
• Knowledge required for resolution
• Distance between anaphor and
antecedent (in NPs, clauses, sentences)
• Number of competing candidates
Fair evaluation
Algorithms should be evaluated on the
basis of the same
• Evaluation data
• Pre-processing tools
Evaluation workbench
Evaluation workbench for anaphora resolution (Mitkov 2000; Barbu and Mitkov 2001)
• Allows the comparison of approaches sharing common principles or similar pre-processing
• Enables the ‘plugging in’ and testing of different anaphora resolution algorithms
All algorithms implemented operate in a fully automatic mode
The need for annotated corpora
Annotated corpora are vital for training
and evaluation
Annotation should cover anaphoric or
coreferential chains and not only anaphor-
antecedent pairs only
Scarce commodity
Lancaster Anaphoric Treebank (100 000 words)
MUC coreference task annotated data (65 000)
Part of the Penn Treebank (90 000)
Additional issues
Annotation scheme
Annotating tools
Annotation strategy
Interannotators’ (dis)agreement is a major issue!
The Wolverhampton coreference annotation project
A 500 000-word corpus annotated for
anaphoric and coreferential links (identity-
of-sense direct nominal anaphora)
Less ambitious in terms of coverage, but
much more consistent
Watch out for the traps!
• Are all annotated data reliable?
• Are all original documents reliable?
• Are all results reported “honest”?
Morale and motivation important!
If I may offer you my advice.... Do not despair if your first evaluation results are
not as high as you wanted them to be Be prepared to provide considerable input in
exchange of minor performance improvement Work hard Be transparent
... and you´ll get there!
Anaphora resolution projects
Ruslan Mitkov’s home page
http://www.wlv.ac.uk/~le1825
Research Group in Computational Linguistics
http://clg.wlv.ac.uk